package com.atguigu.gmall.realtime.utils;

import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.connector.kafka.sink.KafkaRecordSerializationSchema;
import org.apache.flink.connector.kafka.sink.KafkaSink;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;

import java.io.IOException;

/**
 * @author Felix
 * @date 2023/11/7
 * 操作kafka的工具类
 */
public class MyKafkaUtil {

    private static final String KAFKA_SERVER = "hadoop102:9092,hadoop103:9092,hadoop104:9092";

    //获取kafkaSource
    public static KafkaSource<String> getKafkaSource(String topic, String groupId) {
        KafkaSource<String> kafkaSource = KafkaSource.<String>builder()
            .setBootstrapServers(KAFKA_SERVER)
            .setTopics(topic)
            .setGroupId(groupId)
            //在生产环境中，为了保证消费的一致性
            // 需要设置偏移量消费位点
            // 从消费组提交的位点开始消费，如果提交位点不存在，使用最新位点
            // .setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.LATEST))
            // 设置消费的隔离级别
            // .setProperty(ConsumerConfig.ISOLATION_LEVEL_CONFIG,"read_committed")
            .setStartingOffsets(OffsetsInitializer.latest())
            //注意：使用Flink默认的反序列化器，没有办法处理从kafka中读取到的空消息，需要自定义反序列器
            // .setValueOnlyDeserializer(new SimpleStringSchema())
            .setValueOnlyDeserializer(new DeserializationSchema<String>() {
                @Override
                public String deserialize(byte[] message) throws IOException {
                    if (message != null) {
                        return new String(message);
                    }
                    return null;
                }

                @Override
                public boolean isEndOfStream(String nextElement) {
                    return false;
                }

                @Override
                public TypeInformation<String> getProducedType() {
                    return TypeInformation.of(String.class);
                }
            })
            .build();
        return kafkaSource;
    }

    //获取kafkaSink
    public static KafkaSink<String> getKafkaSink(String topic) {
        KafkaSink<String> kafkaSink = KafkaSink.<String>builder()
            .setBootstrapServers(KAFKA_SERVER)
            .setRecordSerializer(KafkaRecordSerializationSchema.builder()
                .setTopic(topic)
                .setValueSerializationSchema(new SimpleStringSchema())
                .build()
            )
            //在生产环境下，要想保证写入到kafka数据的一致性，必须做如下操作
            //开启检查点
            //设置DeliveryGuarantee.EXACTLY_ONCE
            // .setDeliveryGuarantee(DeliveryGuarantee.EXACTLY_ONCE)
            //设置事务前缀
            // .setTransactionalIdPrefix("xxx")
            //在消费端，设置事务的读取隔离级别为读已提交 // .setProperty(ConsumerConfig.ISOLATION_LEVEL_CONFIG,"read_committed")
            //设置事务的超时时间
            // .setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, 15*60*1000 + "")
            .build();
        return kafkaSink;
    }

    //获取kafkaSink对象
    public static <T>KafkaSink<T> getKafkaSinkBySchema(KafkaRecordSerializationSchema<T> krs) {
        KafkaSink<T> kafkaSink = KafkaSink.<T>builder()
            .setBootstrapServers(KAFKA_SERVER)
            .setRecordSerializer(krs)
            //在生产环境下，要想保证写入到kafka数据的一致性，必须做如下操作
            //开启检查点
            //设置DeliveryGuarantee.EXACTLY_ONCE
            // .setDeliveryGuarantee(DeliveryGuarantee.EXACTLY_ONCE)
            //设置事务前缀
            // .setTransactionalIdPrefix("xxx")
            //在消费端，设置事务的读取隔离级别为读已提交 // .setProperty(ConsumerConfig.ISOLATION_LEVEL_CONFIG,"read_committed")
            //设置事务的超时时间
            // .setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, 15*60*1000 + "")
            .build();
        return kafkaSink;
    }

    //获取从topic_db主题中读取数据  创建动态表的建表语句
    public static String getTopicDbDDL(String groupId) {
        return "CREATE TABLE topic_db (\n" +
            "    `database` string,\n" +
            "    `table` string,\n" +
            "    `type` string,\n" +
            "    `data` map<string,string>,\n" +
            "    `old` map<string,string>,\n" +
            "    ts string ,\n" +
            "    proc_time as proctime()\n" +
            ") " + getKafkaDDL("topic_db", groupId);
    }

    //获取kafka连接器连接属性
    public static String getKafkaDDL(String topic, String groupId) {
        return " WITH (\n" +
            "  'connector' = 'kafka',\n" +
            "  'topic' = '" + topic + "',\n" +
            "  'properties.bootstrap.servers' = '" + KAFKA_SERVER + "',\n" +
            "  'properties.group.id' = '" + groupId + "',\n" +
            //在生产环境下，为了保证消费的一致性，需要做如下的配置
            //"  'scan.startup.mode' = 'group-offsets',\n" +
            //"  'properties.auto.offset.reset' = 'latest',\n" +
            "  'scan.startup.mode' = 'latest-offset',\n" +
            "  'format' = 'json'\n" +
            ")";
    }

    //获取upsert-kafka连接器的连接属性
    public static String getUpsertKafkaDDL(String topic) {
        return " WITH (\n" +
            "  'connector' = 'upsert-kafka',\n" +
            "  'topic' = '" + topic + "',\n" +
            "  'properties.bootstrap.servers' = '" + KAFKA_SERVER + "',\n" +
            "  'key.format' = 'json',\n" +
            "  'value.format' = 'json'\n" +
            ")";
    }
}
